Name
Abstract | ||
|---|---|---|
An approach to the automatic derivation of drainage networks from digital elevation models (DEMs) using regular-gridded data is described. Features such as depressions and plateaus, on which drainage directions are undefined, are handled as entities in their own right, and are identified using operations on connected regions. Such an approach is a departure from the traditional pixel-based (local) approaches and facilitates the construction of a consistent drainage network. A single or multiple flow drainage model can be implemented. Accumulation and watershed delineation of the drainage network are performed by recursively climbing ascending paths from sink points. The algorithms are significantly faster than those of other approaches and operate within an efficient buffering scheme which facilitates handling of large DEMs and multiple data sources. |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1080/02693799308901956 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SYSTEMS |
Keywords | Field | DocType |
digital elevation model | Drainage,Data mining,Multiple data,Computer science,Flow (psychology),Digital elevation model,Watershed,Pixel,Feature based,Recursion | Journal |
Volume | Issue | ISSN |
7 | 3 | 0269-3798 |
Citations | PageRank | References |
9 | 1.37 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. E. Mccormack | 1 | 9 | 1.37 |
| Mark Gahegan | 2 | 571 | 55.38 |
| Steven A. Roberts | 3 | 19 | 3.69 |
| J. Hogg | 4 | 67 | 15.52 |
| B. S. Hoyle | 5 | 12 | 2.12 |